Predicting Audience Exposure to Television in Today's Media Environment: An Empirical Integration of Active-Audience and Structural Theories

Abstract

Scholars have long advocated empirical integration of active-audience and structural theories to best explain audience exposure to television. This study incorporated both uses and gratifications and structural variables in an integrated model of audience exposure. Results indicated that seven statistically significant factors—ritualistic motivations, use of the Internet, audience availability, the cost of multi-channel service, age, instrumental motivations, and gender—combined to explain 30.3% of the variance in audience exposure to television. Findings suggest that no single theoretical construct explains the complexities that determine exposure to television. Future inquiry should continue to seek theoretical and empirical integration as a way to understand and explain media behavior.

Notes

Notes: *p < .05;

**p < .01;

***p < .001.

***p < .001;

**p < .01.

¹With this Web-based survey, none of the questions was designed as forced response. Thus, respondents might miss/forget to answer one or a few questions here or there. Replacing missing values with means is a better approach compared to dropping the cases for several reasons. First, though there are very few missing values, the missing values are scattered throughout cases and variables. Deletion of cases can mean substantial loss of subjects. In addition, if cases with missing values are not randomly distributed through the data, deleting all the cases with missing values may result in distortions of the sample. Mean substitution has been widely used to deal with missing values (Tabachnick & Fidell, 2001). Although the method has its own limitations, it is conservative. The mean for the distribution as a whole will not change, and the size and composition of the sample will not change.